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. 2017 Mar 13;8(23):37263–37277. doi: 10.18632/oncotarget.16146

XPG gene polymorphisms and cancer susceptibility: evidence from 47 studies

Jiawen Huang 1, Xiaoqi Liu 2, Ling-Ling Tang 3, Jian-Ting Long 4, Jinhong Zhu 5, Rui-Xi Hua 4, Jufeng Li 1
PMCID: PMC5513715  PMID: 28416771

Abstract

Xeroderma pigmentosum group G (XPG) is a single-strand-specific DNA endonuclease that functions in the nucleotide excision repair pathway. Genetic variations in XPG gene can alter the DNA repair capacity of this enzyme. We evaluated the associations between six single nucleotide polymorphisms (SNPs) in XPG (rs1047768 T>C, rs2296147 T>C, rs2227869 G>C, rs2094258 C>T, rs751402 C>T, and rs873601 G>A) and cancer risk. Forty-seven studies were identified in searches of the PubMed, Scopus, Web of Science, China National Knowledge Infrastructure, and WanFang databases. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a fixed or random effects model. We found that rs873601 G>A was associated with an increased overall cancer risk (AA vs. GG: OR = 1.14, 95% CI = 1.06–1.24; GA/AA vs. GG: OR = 1.08, 95% CI = 1.02–1.15; A vs. G: OR = 1.06, 95% CI = 1.02–1.10). In a stratified analysis, rs1047768 T>C was associated with an increased risk of lung cancer, rs2227869 G>C was associated with a decreased risk of cancer in population-based studies, and rs751402 C>T and rs873601 G>A were associated with the risk of gastric cancer. Our data indicate that rs873601 G>A is associated with cancer susceptibility.

Keywords: XPG, polymorphism, cancer, meta-analysis

INTRODUCTION

There were an estimated 14.1 million new cancer cases and 8.2 million cancer-related deaths in 2012 worldwide [1, 2]. Although recent advances in the diagnosis and treatment of various cancers have improved patient prognosis, most malignancies still impose a heavy burden on society. Cancer is a multifactorial, chronic disease caused by both endogenous (genetic, immune, and endocrine disorders) and exogenous factors (environmental carcinogens and unhealthy behaviors) [1]. Among these etiological factors, gene-environment interactions have been shown to play key roles in cancer development.

The maintenance of genomic integrity is essential for human health. However, DNA damage can occur due to exposure to various chemicals, environmental agents, and ultraviolet radiation. DNA damage can also occur naturally. For example, metabolic processes can generate compounds that damage DNA, which include reactive oxygen and reactive nitrogen species. There are five major DNA damage repair pathways in humans: nucleotide excision repair (NER), base excision repair, double-strand break repair, mismatch repair, and homologous recombination [3]. Failure to properly repair DNA damage can lead to tumorigenesis. The versatile NER pathway is responsible for excising DNA lesions including cross-links, bulky adducts, thymidine dimers, alkylating damage, and oxidative DNA damage [3].

There are at least eight core functional genes in the NER pathway. These include Excision repair cross complementing group 1 (ERCC1) and Xeroderma pigmentosum group (XP) A-G. XPG, also known as ERCC5, is located on chromosome 13q22-q33 [4]. The XPG gene encodes a single-strand specific DNA endonuclease of 1,186 amino acids that cleaves the damaged DNA strand at the 3’ end [5]. Defects in the XPG gene can impair DNA repair resulting in genomic instability and carcinogenesis [6]. Single nucleotide polymorphisms (SNPs) in the XPG gene have been associated with various cancers including colorectal [7], lung [8, 9], gastric [10, 11], and laryngeal [12]. However, different studies have achieved conflicting results. For example, Duan et al. found that rs2296147 T>C in XPG was associated with an increased risk of gastric cancer [13], but this association was not replicated in other studies [10, 11]. The discordances might be attributed to the limited sample sizes of individual studies, different sources of controls, and ethnic variation. In this study, we performed a meta-analysis of the associations between six potentially functional SNPs: rs1047768 T>C, rs2296147 T>C, rs2227869 G>C, rs2094258 C>T, rs751402 C>T, and rs873601 G>A in the XPG gene and the risk of cancer.

RESULTS

Study characteristics

A total of 215 articles were identified using the Web of Science, Scopus, and PubMed. An additional 26 potential relevant articles were identified in the CNKI and WanFang databases. After screening the titles and abstracts, 135 studies remained for further full-text review. We excluded 17 meta-analyses and reviews as well as 69 studies that did not assess the SNPs of interest. A detailed assessment was then performed of 49 studies. Two of these studies were removed, one because there was a lack of detailed genotype data and the other because of study population overlap. The final meta-analysis included 47 articles. There were 22 articles with 12,833 cases and 151,86 controls for rs1047768 T>C [7–9, 12, 14–31], 14 studies with 11,327 cases and 12,684 controls for rs2296147 T>C [9–11, 13, 18, 24, 26–28, 32–37], 11 studies with 5,898 cases and 7,448 controls for rs2227869 G>C [8, 9, 14, 17, 18, 20, 22, 25, 38–40], 17 studies with 9,826 cases and 10,552 controls for rs2094258 C>T [10, 11, 18, 24, 26–28, 34–37, 41–46], 21 studies with 10,369 cases and 11,207 controls for rs751402 C>T [10, 13, 24, 26–29, 31, 32, 36, 37, 42–45, 47–52], and 14 studies with 10,873 cases and 12,535 controls for rs873601 G>A [9–11, 18, 24, 26–28, 32, 34, 36, 52–54]. A flow chart summarizing the process of relevant study identification is shown in Figure 1, and the study characteristics are shown in Table 1.

Figure 1. Flow diagram showing the process used to identify eligible studies.

Figure 1

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Table 1. Characteristics of the studies included in the meta-analysis.

Author Year Country Ethnicity Source Cancer Case Control MAF HWE Score
BB Bb bb All BB Bb bb All
rs1047768 T>C
Shen M 2005 China Asian PB Lung 55 49 14 118 63 36 13 112 0.28 0.037 10
Zienolddiny S 2006 Norway Caucasian PB Lung 60 119 137 316 109 126 138 373 0.54 <0.001 11
Moreno V 2006 Spain Caucasian HB Colorectal 114 184 53 351 105 164 51 320 0.42 0.325 11
Garcia-Closas M 2006 Spain Caucasian HB Bladder 188 530 385 1103 222 506 366 1094 0.57 0.052 12
Xie WM 2007 China Asian PB HCC 194 195 38 427 235 196 48 479 0.30 0.451 11
Abbasi R 2009 Germany Caucasian PB Laryngeal 43 127 78 248 115 320 212 647 0.57 0.762 13
Hussain SK 2009 China Asian PB Gastric 97 61 12 170 189 168 29 386 0.29 0.173 13
Ma H 2012 USA Caucasian HB SCCHN 184 506 369 1059 179 507 379 1065 0.59 0.669 11
Sakoda LC 2012 USA Caucasian PB Lung 108 378 256 742 245 722 507 1474 0.59 0.656 15
He J 2013 China Asian HB Gastric 571 469 85 1125 610 474 112 1196 0.29 0.155 13
Paszkowska-Szczur K 2013 Poland Caucasian PB Melanoma 128 291 214 633 242 623 465 1330 0.58 0.189 13
Li X 2014 China Asian HB Laryngeal 49 101 60 210 46 97 67 210 0.55 0.333 9
Mirecka A 2014 Poland Caucasian HB Prostate 128 272 221 621 154 368 259 781 0.57 0.260 9
Li XC 2014 China Asian HB Gastric 37 95 85 217 29 93 95 217 0.65 0.414 8
Na N 2015 China Asian HB Breast 161 140 24 325 171 134 20 325 0.27 0.352 10
Paszkowska-Szczur K 2015 Poland Caucasian HB Colorectal 104 221 138 463 242 623 465 1330 0.58 0.189 9
He J 2016 China Asian HB Neuroblastoma 135 93 20 248 307 198 26 531 0.24 0.409 10
Hua RX 2016 China Asian HB Colorectal 970 758 173 1901 1023 812 142 1977 0.28 0.266 10
Hua RX 2016 China Asian HB Gastric 607 445 90 1142 625 461 87 1173 0.27 0.875 11
Li RJ 2016 China Asian HB Gastric 57 92 67 216 68 87 61 216 0.48 0.004 7
Wang MY 2016 China Asian HB Prostate 491 433 80 1004 534 440 81 1055 0.29 0.461 10
Bai Y 2016 China Asian HB Gastric 41 98 55 194 32 106 87 225 0.62 0.975 6
rs2296147 T>C
Shao MH 2007 China Asian HB Lung 570 304 52 926 590 358 31 979 0.21 0.008 10
Doherty JA 2011 USA Mixed PB Endometrial 194 356 165 715 199 364 157 720 0.47 0.696 11
Duan Z 2012 China Asian HB Gastric 257 122 24 403 260 132 11 403 0.19 0.232 11
He J 2012 China Asian HB Gastric 700 371 54 1125 742 398 56 1196 0.21 0.779 13
Ma H 2012 USA Caucasian HB SCCHN 280 532 244 1056 294 543 228 1065 0.47 0.440 11
Sakoda LC 2012 USA Caucasian PB Lung 182 385 174 741 407 723 341 1471 0.48 0.565 15
Zhu ML 2012 China Asian HB ESCC 757 305 53 1115 699 368 50 1117 0.21 0.860 13
Yang WG 2012 China Asian HB Gastric 208 105 24 337 196 110 41 347 0.28 <0.001 9
Yang B 2013 China Asian HB Prostate 37 49 143 229 25 46 167 238 0.80 <0.001 8
Na N 2015 China Asian HB Breast 188 104 33 325 199 98 28 325 0.24 0.003 9
Sun Z 2015 China Asian HB NPC 119 177 76 372 111 180 80 371 0.46 0.660 11
Chen YZ 2016 China Asian HB Gastric 442 217 33 692 475 264 32 771 0.21 0.535 11
He J 2016 China Asian HB Neuroblastoma 160 79 9 248 343 170 18 531 0.19 0.583 10
Hua RX 2016 China Asian HB Colorectal 1169 644 88 1901 1213 692 72 1977 0.21 0.027 9
Hua RX 2016 China Asian HB Gastric 725 364 53 1142 746 388 39 1173 0.20 0.182 11
rs2227869 G>C
Shen M 2005 China Asian PB Lung 103 14 1 118 100 11 0 111 0.05 0.583 11
Garcia-Closas M 2006 Spain Caucasian HB Bladder 1050 91 2 1143 1046 90 0 1136 0.04 0.164 12
Huang WY 2006 USA Caucasian PB Colorectal 598 52 1 651 601 60 1 662 0.05 0.694 14
Hooker S 2008 USA African HB Prostate 234 20 0 254 274 27 0 301 0.05 0.415 7
Hussain SK 2009 China Asian PB Gastric 174 13 0 187 314 56 3 372 0.08 0.773 13
Ma H 2012 USA Caucasian HB SCCHN 987 70 2 1059 974 90 2 1066 0.04 0.958 11
Sakoda LC 2012 USA Caucasian PB Lung 1 63 680 744 2 110 1362 1474 0.96 0.886 15
Santos LS 2013 Portugal Caucasian HB Thyroid 99 6 1 106 184 27 1 212 0.02 0.993 8
Paszkowska-Szczur K 2013 Poland Caucasian PB Melanoma 567 67 2 636 1168 162 2 1332 0.06 0.137 13
Mirecka A 2014 Poland Caucasian HB Prostate 485 83 3 571 682 99 1 782 0.06 0.181 9
Paszkowska-Szczur K 2015 Poland Caucasian HB Colorectal 372 55 2 429 1168 162 2 1332 0.06 0.137 9
rs2094258 C>T
He J 2012 China Asian HB Gastric 457 518 150 1125 457 560 179 1196 0.62 0.728 13
Ma H 2012 USA Caucasian HB SCCHN 706 295 37 1038 721 291 41 1053 0.82 0.092 11
Yang WG 2012 China Asian HB Gastric 131 149 57 337 145 166 36 347 0.66 0.252 10
Zhu ML 2012 China Asian HB ESCC 414 524 177 1115 424 525 168 1117 0.61 0.793 13
Yang B 2013 China Asian HB Prostate 61 75 93 229 58 75 105 238 0.40 <0.001 9
Na N 2015 China Asian HB Breast 102 157 66 325 131 147 47 325 0.63 0.581 10
Sun Y 2015 China Asian HB Laryngeal 140 106 25 271 152 101 18 271 0.75 0.826 11
Sun Z 2015 China Asian HB NPC 209 68 95 372 211 66 94 371 0.66 <0.001 10
Chen YZ 2016 China Asian HB Gastric 287 304 101 692 291 368 112 771 0.62 0.803 11
He J 2016 China Asian HB Neuroblastoma 116 93 39 248 203 254 74 531 0.62 0.701 10
Hua RX 2016 China Asian HB Colorectal 797 856 248 1901 899 881 197 1977 0.68 0.378 10
Feng YB 2016 China Asian HB Gastric 15 75 87 177 15 96 127 238 0.26 0.577 6
Hua RX 2016 China Asian HB Gastric 499 508 135 1142 527 524 122 1173 0.67 0.623 11
Lu JJ 2016 China Asian HB Gastric 17 67 100 184 13 72 121 206 0.24 0.605 6
Ma SH 2016 China Asian HB Breast 27 136 157 320 15 96 127 238 0.26 0.577 7
Yang LQ 2016 China Asian HB Gastric 71 74 10 155 121 111 14 246 0.72 0.076 6
Ying MF 2016 China Asian HB Pancreatic 87 92 16 195 117 115 22 254 0.69 0.400 7
rs751402 C>T
Shao MH 2007 China Asian HB Lung 105 429 433 967 110 425 448 983 0.67 0.544 11
Yoon AJ 2011 Taiwan Asian HB HCC 11 52 33 96 32 137 167 336 0.70 0.614 6
Duan Z 2012 China Asian HB Gastric 47 181 172 400 29 165 206 400 0.72 0.605 11
He J 2012 China Asian HB Gastric 148 491 486 1125 137 499 560 1196 0.68 0.110 13
Zavras AI 2012 Taiwan Mixed HB OSCC 31 110 98 239 32 137 167 336 0.70 0.614 9
Meng X 2013 China Asian HB Salivary gland 11 63 59 133 23 55 64 142 0.64 0.065 8
Na N 2015 China Asian HB Breast 45 152 128 325 41 147 137 325 0.65 0.872 10
Sun Z 2015 China Asian HB NPC 237 118 17 372 235 117 19 371 0.21 0.377 11
Wang H 2016 China Asian HB Breast 1 10 90 101 11 39 51 101 0.70 0.398 9
Chen YZ 2016 China Asian HB Gastric 93 313 286 692 89 331 351 771 0.67 0.416 11
He J 2016 China Asian HB Neuroblastoma 38 114 96 248 82 241 208 531 0.62 0.380 10
Hua RX 2016 China Asian HB Colorectal 248 860 792 1900 301 952 724 1977 0.61 0.680 10
Guo BW 2016 China Asian HB Gastric 22 73 47 142 21 136 117 274 0.68 0.029 5
Feng YB 2016 China Asian HB Gastric 24 83 70 177 28 107 101 236 0.65 0.967 6
Hua RX 2016 China Asian HB Gastric 161 555 426 1142 189 551 433 1173 0.60 0.537 11
Li RJ 2016 China Asian HB Gastric 22 106 88 216 18 103 95 216 0.68 0.174 8
Lu JJ 2016 China Asian HB Gastric 24 91 69 184 22 97 87 206 0.66 0.510 6
Ma SH 2016 China Asian HB Breast 43 150 127 320 28 101 107 236 0.67 0.580 7
Yang LQ 2016 China Asian HB Gastric 33 73 49 155 32 111 103 246 0.64 0.807 6
Wang MY 2016 China Asian HB Prostate 104 458 442 1004 111 467 477 1055 0.67 0.834 10
Zhou RM 2016 China Asian HB Gastric 61 196 174 431 46 193 193 432 0.67 0.827 12
rs873601 G>A
Shao MH 2007 China Asian HB Lung 260 493 220 973 277 494 217 988 0.47 0.907 11
He J 2012 China Asian HB Gastric 274 560 291 1125 327 605 264 1196 0.47 0.616 13
Ma H 2012 USA Caucasian HB SCCHN 66 427 565 1058 83 411 572 1066 0.73 0.445 11
Sakoda LC 2012 USA Caucasian PB Lung 51 299 392 742 107 584 783 1474 0.73 0.894 15
Yang WG 2012 China Asian HB Gastric 96 163 78 337 91 164 91 346 0.50 0.333 10
Zhu ML 2012 China Asian HB ESCC 314 566 235 1115 311 565 241 1117 0.47 0.601 13
Na N 2015 China Asian HB Breast 99 156 70 325 109 150 66 325 0.43 0.276 10
Zhao F 2015 China Asian HB Pancreatic 105 111 30 246 118 107 21 246 0.30 0.637 8
Chen YZ 2016 China Asian HB Gastric 172 333 187 692 205 396 170 771 0.48 0.415 11
He J 2016 China Asian HB Neuroblastoma 70 112 66 248 137 270 124 531 0.49 0.686 10
Wang B 2016 China Asian HB HCC 163 271 104 538 271 408 214 893 0.47 0.014 12
Hua RX 2016 China Asian HB Colorectal 476 954 471 1901 550 1025 402 1977 0.46 0.057 10
Hua RX 2016 China Asian HB Gastric 311 557 274 1142 323 598 252 1173 0.47 0.424 11
Zhou RM 2016 China Asian HB Gastric 115 215 101 431 132 200 100 432 0.46 0.152 12
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Abbreviations: HB, hospital-based; PB, population-based; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium; HCC, hepatocellular carcinoma; SCCHN, squamous cell carcinoma of the head and neck; ESCC, esophageal squamous cell carcinoma; OSCC, oral squamous cell carcinoma; NPC, nasopharyngeal carcinoma.

Meta-analysis results

We observed no significant association between rs1047768 T>Cand overall cancer risk (Table 2). However, in stratified analysis, rs1047768 T>C was associated with an increased risk of lung cancer under homozygous [odds ratio (OR) = 1.32, 95% confidence interval (CI) = 1.06–1.64], heterozygous (OR = 1.35, 95% CI = 1.10–1.65), dominant (OR = 1.35, 95% CI = 1.12–1.63), and allele contrast (OR = 1.14, 95% CI = 1.02–1.27) models.

Table 2. Associations between the six SNPs in the XPG gene and cancer risk.

Variables No. of studies No. of cases No. of controls Homozygous Heterozygous Recessive Dominant Allele
OR(95% CI) P het OR(95% CI) P het OR(95% CI) P het OR(95% CI) P het OR(95% CI) P het
rs1047768 T>C CC vs. TT CT vs. TT CC vs. CT/TT CC/CT vs. TT C vs. T
All 22 12833 15186 1.03 (0.95–1.11) 0.010 1.03 (0.97–1.09) 0.192 1.00 (0.93–1.07) 0.171 1.03 (0.98–1.09) 0.038 1.01 (0.98–1.05) 0.012
Ethnicity
 Caucasian 9 5536 7084 1.03 (0.88–1.21) 0.012 1.04 (0.95–1.14) 0.061 1.00 (0.93–1.07) 0.344 1.04 (0.90–1.20) 0.011 1.01 (0.94–1.10) 0.011
 Asian 13 7297 8102 1.03 (0.92–1.16) 0.081 1.02 (0.96–1.10) 0.493 1.00 (0.90–1.11) 0.116 1.03 (0.96–1.10) 0.304 1.02 (0.97–1.07) 0.105
Cancer type
 Lung 3 1176 1959 1.32 (1.06–1.64) 0.175 1.35 (1.10–1.65) 0.278 1.08 (0.92–1.26) 0.360 1.35 (1.12–1.63) 0.172 1.14 (1.02–1.27) 0.059
 Colorectal 3 2715 3627 0.95 (0.63–1.45) 0.006 0.96 (0.86–1.08) 0.480 0.99 (0.70–1.39) 0.012 0.94 (0.78–1.14) 0.133 0.99 (0.91–1.07) 0.020
 Gastric 6 3064 3413 0.88 (0.74–1.05) 0.118 0.98 (0.88–1.09) 0.263 0.88 (0.74–1.05) 0.279 0.97 (0.87–1.07) 0.127 0.93 (0.82–1.04) 0.073
 Others 10 5878 7517 1.04 (0.93–1.15) 0.507 1.05 (0.96–1.14) 0.670 1.01 (0.93–1.10) 0.725 1.05 (0.97–1.14) 0.628 1.03 (0.98–1.08) 0.659
rs2296147 T>C CC vs. TT CT vs. TT CC vs. CT/TT CC/CT vs. TT C vs. T
All 15 11327 12684 1.10 (1.00–1.12) 0.068 0.95 (0.90–1.01) 0.480 1.08 (0.99–1.18) 0.057 0.97 (0.92–1.03) 0.297 1.00 (0.96–1.04) 0.118
 Gastric 5 3699 3890 1.11 (0.76–1.60) 0.026 0.95 (0.86–1.04) 0.945 1.13 (0.78–1.63) 0.025 0.96 (0.88–1.06) 0.697 0.99 (0.91–1.07) 0.197
rs2227869 G>C CC vs. GG GC vs. GG CC vs. GC/GG GC/CC vs. GG C vs. G
All 11 5898 7448 1.67 (0.82–3.41) 0.924 0.90 (0.80–1.02) 0.153 0.98 (0.73–1.32) 0.699 0.92 (0.81–1.03) 0.108 0.93 (0.83–1.04) 0.079
 PB 5 2336 3951 1.08 (0.37–3.10) 0.793 0.80 (0.65–0.99) 0.239 0.89 (0.65–1.21) 0.766 0.81 (0.66–1.00) 0.170 0.84 (0.71–0.99) 0.115
 HB 6 3562 4829 2.46 (0.91–6.67) 0.852 0.96 (0.82–1.11) 0.198 2.48 (0.91–6.74) 0.865 0.98 (0.84–1.13) 0.190 1.00 (0.87–1.15) 0.202
rs2094258 C>T TT vs. CC CT vs. CC TT vs. CT/CC CT/TT vs. CC T vs. C
All 17 9826 10552 1.09 (1.00–1.19) 0.025 1.00 (0.94–1.07) 0.314 1.07 (0.99–1.16) 0.089 1.02 (0.97–1.09) 0.081 1.03 (0.99–1.08) 0.015
 Gastric 7 3812 4177 0.99 (0.86–1.15) 0.083 0.95 (0.86–1.05) 0.734 1.01 (0.89–1.14) 0.119 0.96 (0.88–1.06) 0.409 0.98 (0.92–1.05) 0.133
rs751402 C>T TT vs. CC CT vs. CC TT vs. CT/CC CT/TT vs. CC T vs. C
All 21 10369 11207 1.18 (1.00–1.39) <0.001 1.10 (0.99–1.23) 0.082 1.02 (0.94–1.10) 0.006 1.11 (0.98–1.25) <0.001 1.08 (0.98–1.18) <0.001
 Gastric 10 4664 5150 1.38 (1.12–1.70) 0.020 1.14 (1.05–1.24) 0.936 1.27 (1.06–1.51) 0.053 1.17 (1.08–1.26) 0.437 1.17(1.07–1.27) 0.043
rs873601 G>A AA vs. GG GA vs. GG AA vs. GA/GG GA/AA vs. GG A vs. G
All 14 10873 12535 1.14 (1.06–1.24) 0.193 1.06 (0.99–1.13) 0.904 1.08 (0.99–1.17) 0.035 1.08 (1.02–1.15) 0.841 1.06 (1.02–1.10) 0.234
 Gastric 5 3727 3918 1.18 (1.04–1.34) 0.333 1.04 (0.93–1.16) 0.663 1.16 (1.04–1.28) 0.263 1.08 (0.98–1.20) 0.578 1.09 (1.02–1.16) 0.336
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No significant association was observed between rs2296147 T>C and overall cancer risk. Similarly, there was no significant association between rs2227869 G>C and overall cancer risk. However, a significant association was identified in population-based studies when the data were stratified based on the source of the controls under heterozygous (OR = 0.80, 95% CI = 0.65–0.99) and allele contrast (OR = 0.84, 95% CI = 0.71–0.99) models. We observed an association between rs2094258 C>T and overall cancer risk under the homozygous model (OR = 1.09, 95% CI = 1.00–1.19), which approached borderline statistical significance. Another borderline significant association was observed between rs751402 C>T and overall cancer risk under the homozygous model (OR = 1.18, 95% CI = 1.00–1.39). In the stratified analysis, a significant association was observed for gastric cancer under homozygous (OR = 1.38, 95% CI = 1.12–1.70), heterozygous (OR = 1.14, 95% CI = 1.05–1.24), recessive (OR = 1.27, 95% CI = 1.06–1.51), dominant (OR = 1.17, 95% CI = 1.08–1.26), and allele contrast (OR = 1.17, 95% CI = 1.07–1.27) models.

A significant association was observed between rs873601 G>A and overall cancer risk under homozygous (OR = 1.14, 95% CI = 1.06–1.24), dominant (OR = 1.08, 95% CI = 1.02–1.15), and allele contrast (OR = 1.06, 95% CI = 1.02-1.10) models (Figure 2). The association with gastric cancer remained statistically significant under homozygous (OR = 1.18, 95% CI = 1.04–1.34), recessive (OR = 1.16, 95% CI = 1.04–1.28), and allele contrast (OR = 1.09, 95% CI = 1.02–1.16) models.

Figure 2. Forest plot of overall cancer risk associated with rs873601 G>A in the XPG gene under an allele contrast model.

Figure 2

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For each study, estimated ORs and 95% CIs are plotted with a box and horizontal line, respectively. (◇, pooled ORs and associated 95% CIs).

Heterogeneity and sensitivity analysis

Study heterogeneity was observed for the association between rs1047768 T>C and overall cancer risk under homozygous, dominant, and allele contrast models (P = 0.010, P = 0.038, and P = 0.012, respectively); rs2094258 C>T under homozygous and allele contrast models (P = 0.025 and P = 0.015, respectively); rs751402 C>T under homozygous, recessive, dominant, and allele contrast models (P < 0.001, P = 0.006, P < 0.001, P < 0.001, respectively); and rs873601 G>A under a recessive model (P = 0.035). These data indicated that the removal of any individual study from the analysis did not qualitatively change the pooled ORs (data not shown).

Publication bias

The Begg's funnel plots of the associations between the SNPs in the XPG gene and cancer risk were basically symmetrical (Figure 3). Egger's tests indicated there was no publication bias for rs1047768 T>C under homozygous (P = 0.107), heterozygous (P = 0.190), recessive (P = 0.325), dominant (P = 0.137), and allele contrast (P = 0.301) models; rs2296147 T>C under homozygous (P = 0.789), heterozygous (P = 0.925), recessive (P = 0.577), dominant (P = 0.464), and allele contrast (P = 0.129) models; rs2227869 G>C under homozygous (P = 0.708), heterozygous (P = 0.289), recessive (P = 0.042), dominant (P = 0.297), and allele contrast (P = 0.197) models; rs2094258 C>T under homozygous (P = 0.387), heterozygous (P = 0.350), recessive (P = 0.844), dominant (P = 0.276), and allele contrast (P = 0.351) models; rs751402 C>T under homozygous (P = 0.107), heterozygous (P = 0.336), recessive (P = 0.137), dominant (P = 0.325), and allele contrast (P = 0.301) models; and rs873601 G>A under homozygous (P = 0.395), heterozygous (P = 0.656), recessive (P = 0.645), dominant (P = 0.811), and allele contrast (P = 0.346) models (Table 3).

Figure 3. Funnel plot of the association between rs873601 G>A in the XPG gene and overall cancer risk under an allele contrast model.

Figure 3

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Each point represents an individual study that reported the indicated association.

Table 3. Publication bias among studies that evaluated the associations between the six SNPs in the XPG gene and cancer susceptibility.

Polymorphism No. of studies Egger's test P values
Homozygous Heterozygous Recessive Dominant Allele contrast
rs1047768 22 0.107 0.190 0.325 0.137 0.301
rs2296147 15 0.789 0.925 0.577 0.464 0.129
rs2227869 11 0.708 0.289 0.042 0.297 0.197
rs2094258 17 0.387 0.350 0.844 0.276 0.351
rs751402 21 0.107 0.336 0.137 0.325 0.301
rs873601 14 0.395 0.656 0.645 0.811 0.346
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False-positive report probability (FPRP) analysis and trial sequential analysis (TSA)

All significant findings remained significant at a prior probability of 0.1, with all the FPRP values less than 0.20 with the exception of the population-designed studies of rs2227869 G>C (Table 4). TSA indicated that the cumulative z-curve crossed the trial sequential monitoring boundary, suggesting that the sample size was sufficient and that no further analysis was required to confirm the results (Figure 4).

Table 4. False-positive report probability values for significant results.

Genotype Crude OR (95% CI) P a Statistical power b Prior probability
0.25 0.1 0.01 0.001 0.0001
rs1047768 T>C (lung cancer)
 CC vs. TT 1.32 (1.06–1.64) 0.012 0.998 0.035 0.097 0.542 0.923 0.992
 CT vs. TT 1.35 (1.10–1.65) 0.004 0.995 0.011 0.033 0.273 0.791 0.974
 CC/CT vs. TT 1.35 (1.12–1.63) 0.002 0.859 0.006 0.019 0.177 0.685 0.956
C vs. T 1.14 (1.02–1.27) 0.017 1.000 0.048 0.130 0.622 0.943 0.994
rs2227869 G>C (population-based studies)
 GC vs. GG 0.80 (0.65–0.99) 0.041 0.987 0.111 0.272 0.805 0.976 0.998
 C vs. G 0.84 (0.71–0.99) 0.041 1.000 0.110 0.271 0.803 0.976 0.998
rs751402 C>T (gastric cancer)
 TT vs. CC 1.38 (1.12–1.70) 0.002 1.000 0.007 0.019 0.179 0.687 0.956
 CT vs. CC 1.14 (1.05–1.24) 0.003 1.000 0.008 0.024 0.213 0.732 0.965
 TT vs. CT/CC 1.27 (1.06–1.51) 0.010 1.000 0.030 0.085 0.506 0.912 0.990
 CT/TT vs. CC 1.17 (1.08–1.26) <0.001 1.000 0.001 0.002 0.019 0.161 0.658
 T vs. C 1.17 (1.07–1.27) 0.001 1.000 0.002 0.006 0.063 0.404 0.871
rs873601 G>A (overall)
 AA vs. GG 1.14 (1.06–1.24) 0.001 1.000 0.002 0.006 0.061 0.394 0.867
 GA/AA vs. GG 1.08 (1.02–1.15) 0.012 1.000 0.036 0.101 0.552 0.926 0.992
 A vs. G 1.06 (1.02–1.10) 0.002 1.000 0.006 0.016 0.155 0.650 0.949
rs873601 G>A (gastric cancer)
 AA vs. GG 1.18 (1.04–1.34) 0.009 1.000 0.027 0.078 0.482 0.904 0.989
 AA vs. GA/GG 1.16 (1.04–1.28) 0.008 1.000 0.022 0.064 0.431 0.884 0.987
 A vs. G 1.09 (1.02–1.16) 0.011 1.000 0.031 0.089 0.517 0.915 0.991
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aChi-square tests were used to assess the genotype frequency distributions.

bStatistical power was calculated using the number of observations in the subgroup and the P values in this table.

Figure 4. TSA of rs873601 G>A in the XPG gene and overall cancer risk under an allele contrast model.

Figure 4

Open in a new tab

DISCUSSION

The NER pathway is critical for the repair of bulky DNA lesions resulting from exposure to chemical carcinogens as well as ionizing radiation in order to maintain genomic integrity and prevent carcinogenesis [55]. Because the XPG gene is an indispensable component of the NER pathway, SNPs in XPG may alter the expression or function of XPG thereby modifying the risk of cancer. Most previous meta-analyses of the association between SNPs in XPG and cancer risk have focused on rs17655 G>C [56–59]. However, recent studies have shown that other SNPs in XPG may also be associated with cancer risk. For example, Chen et al. found that rs873601 G>A was associated with an increased risk of gastric cancer in a Chinese Han population [36]. Wang et al. found that rs751402 C>T was protective against breast cancer in Chinese Han women [47]. Additionally, the T allele of rs2296147 was associated with an increased risk of prostate cancer [35]. However, the results of previous studies have been inconsistent, possibly due to variations in the study populations and limited sample sizes. We therefore performed a meta-analysis of 47 studies to comprehensively evaluate the associations between six SNPs in XPG: rs1047768 T>C, rs2296147 T>C, rs2227869 G>C, rs2094258 C>T, rs751402 C>T, and rs873601 G>A and cancer risk.

The rs873601 G>A polymorphism is located in a miRNA binding site in the XPG gene. Thus, it may alter XPG expression by modulating the miRNA-mRNA interaction, which could play a role in carcinogenesis [10]. We demonstrated that rs873601 G>A was significantly associated with overall cancer risk. Individuals with the AA genotype of rs873601 had a 1.14-fold higher risk of cancer compared to individuals with the GG genotype. Similar results were obtained for gastric cancer. The A allele of rs873601 was previously shown to result in reduced mRNA expression of XPG in both adjacent normal gastric cancer tissue and normal cell lines in a recessive manner [10]. These findings provide insight into the molecular mechanisms by which the AA genotype of rs873601 may increase the risk of gastric cancer.

The rs751402 C>T polymorphism is located in the E2F1/YY1 binding and response site in the proximal promoter region of XPG [60]. This variant might reduce the DNA repair capacity of XPG by disrupting the DNA binding motifs and altering transcription factor affinities [47]. In our study, rs751402 C>T was significantly associated with overall cancer risk. The TT genotype of rs751402 was associated with an 18% increase in cancer risk compared to the CC genotype. Moreover, a significant association was observed between rs751402 C>T and gastric cancer risk under all genetic models. The rs751402 C>T polymorphism is likely to influence cancer risk by regulating XPG expression, but its effect on XPG function is not yet clear [47].

The rs2094258 C>T polymorphism is located in a transcription factor binding site in the 5’ region of the XPG gene. We found that the association between rs2094258 C>T and overall cancer risk was borderline significant. Individuals with the TT genotype of rs2094258 had a 9% higher risk of cancer compared to those with the CC genotype. However, the association was not significant in gastric cancer, indicating that it may not impact gastric cancer risk. Significant associations were observed among some subgroups for all other selected SNPs. We found that the C allele of rs1047768 may increase the risk of lung cancer. Moreover, the C allele of rs2227869 significantly reduced cancer risk in population-based studies. No statistically significant association was observed between rs2296147 T>C and overall cancer risk.

Although we found significant associations between SNPs in the XPG gene and cancer risk, our study had several limitations. First, although Egger's tests showed no obvious publication bias, some bias was unavoidable since only studies published in English and Chinese were included in our meta-analysis. Second, we observed significant heterogeneity in some of our analyses, which is a common drawback of a meta-analysis. Third, due to a lack of sufficient individual data, we were unable to perform multivariate analysis with adjustment for potential confounding factors such as tobacco use, alcohol consumption, and other carcinogenic factors.

Our study is the first meta-analysis of the association between the six selected SNPs in XPG gene and cancer risk. The results indicate that the AA genotype of rs873601 increases overall cancer risk. Additionally, rs751402 C>T and rs873601 G>A were associated with gastric cancer risk. Finally, rs1047768 T>C was found to confer susceptibility to lung cancer. Further epidemiological investigations with larger sample sizes are warranted to validate our findings. Functional studies are also required to elucidate the mechanisms by which these SNPs modify cancer risk.

MATERIALS AND METHODS

Study identification

We searched multiple databases including PubMed, Scopus, Web of Science, CNKI, and the WanFang database using combinations of keywords such as “XPG”, “polymorphism”, and “cancer” as well as synonyms “Xeroderma pigmentosum group G, ERCC5 or Excision repair cross complementing group 5”, “variant or variation”, and “tumor, neoplasm, or carcinoma”. Human studies published before December 20, 2016 in either English or Chinese were included. The reference lists in eligible studies and review articles were examined in order to identify additional relevant studies. In cases of study population overlap, the study with the largest sample size was selected.

Inclusion and exclusion criteria

All studies included in this analysis were required to meet the following criteria: (1) study of the associations between any of the six potentially functional SNPs: rs1047768 T>C, rs2296147 T>C, rs2227869 G>C, rs2094258 C>T, rs751402 C>T, and rs873601 G>A in the XPG gene and cancer risk; (2) case-control study; and (3) sufficient genotype data available to calculate ORs and 95% CIs. The exclusion criteria were: (1) studies conducted in the same or overlapping population and (2) review article or conference report.

Data extraction

Key information was independently extracted from eligible studies by two investigators and included the following items: the first author, year of publication, type of cancer, country, ethnicity, control source, number of cases and controls, the quantity of each genotype in cases and controls, minor allele frequency (MAF), and the Hardy-Weinberg equilibrium (HWE) test P value for the control subjects. Disagreements regarding these items were resolved through discussion.

Statistical analysis

Chi-square tests were used to test deviation from HWE in the study control groups. Genetic associations between the six selected SNPs in the XPG gene and cancer risk were assessed using the crude ORs and corresponding 95% CIs under homozygous, heterozygous, recessive, dominant, and allele contrast models. Heterogeneity between studies was assessed using the Q and I2 values. A random effects model was adopted to calculate the pooled OR and 95% CI in the case of Phet < 0.1 or I2 > 50%. Otherwise, a fixed effects model was applied. Stratified analyses were conducted by ethnicity (Asians and Caucasians), source of control [population-based (PB) or hospital-based (HB)], and cancer type.

Sensitivity analyses were performed to assess the influence of the individual studies on the pooled OR by sequentially removing one study at a time and recalculating the pooled OR. Egger's tests were used to evaluate publication bias. FPRP analysis [61, 62] and TSA were performed as described previously [63]. All statistical analyses were performed using the STATA 12.0 software (Stata Corporation, College Station, TX, USA). All statistics were two-sided. P values < 0.05 were considered statistically significant.

Footnotes

CONFLICTS OF INTEREST

The authors declare that there are no conflicts of interest.

FUNDING

This study was supported by a grant from the Natural Science Foundation of Guangdong Province (No. 2015A030310324).

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